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Symbol Kurs

2022-11-25 to 2022-12-09 CAS ADS M5

Reiter

CAS Applied Data Science Module 5 - Peer Exchange Group
 
A group of 4-6 CAS particpants meets and discusses 30 min about a prepared case from each participant.

Learning outcomes 
  • Analysis and review of data science challenges.
  • Communication of data science challenges.
Target group
  • Graduates and professionals enrolled for the CAS Applied Data Science 
Prerequisites
  • Paricipated in about 80% of the CAS ADS programme. Submitted (uploaded to this Ilias course) case for discussion (max half a page).
Case submssion
  • Submit max half a page (11pt) about your case to be discussed to cas-ads.math@lists.unibe.ch . It should be a real experience from work, the CAS or other projects. Please don't forget title and name.
  • Methods Moderated round table discussions.
  • Moderated round table discussions.
Time : 2022-11-25, 2022-12-02 and 2022-12-09. 09:00-12:30
Location : University of Bern, Parkterasse 14, room 323 and online. 

Language: English
Participants : Max 24
Registration : Mandatory (via Ilias)
Responsible : PD Dr. Sigve Haug
Prerequisites: Your Module 3 notebook(s). An idea for a final CAS project.

In this session you will randomly be assigned to a course colleague. The colleague will discuss with you her notebook(s) from the Module 3 project. Then she will present her idea for a final CAS project. For this you have 80 Minutes (till the break). After the break you change roles.

Afterwards at home, you write a peer consulting report (PDF) based on the discussion. The peer consulting report (PDF) is the assessment for Module 5. The deadline for uploading this report to the Reports folder here on Ilias is December 31. At the same time you also email the report to your colleague. 

The report should be in English or German and 3-5 pages long with font size 11 and 1.5 spacing. It should contain sufficent metadata (your name, contact, etc, also of your colleague) and references you have used or suggest for your colleague. 

First you describe the Module 3 task of your colleague in about half a page, then you point out good solutions (about one page) and aspects and then you suggest improvements (about one page). The same you do for the final project idea.

Schedule

09:00 - 09:10 Introduction
09:10 - 10:30 Peer exchange in pairs
10:30 - 11:00 Break
11:00 - 12:20 Peer exchange in pairs
12:20 - 12:30 Group creations for Selected Readings 1
We discuss this Article: 

https://plato.stanford.edu/entries/artificial-intelligence/

Each group prepares a set of google slides summarising the chapters assigned to the group. Each summary can have a maximum of 10 slides. Groups self organize their division of work and presentation. 

Schedule (CX denotes chapters in the article, login to see the links to the groups' slides):

09:00 Welcome
09:15 C1 C2 Presentation Group 1 () 5
09:50 C3 C4 C5 Presentation Group 2 () 6
10:30 Break
11:00 C6 C7 Presentation Group 3 () 5
11:35 C8 C9 Presentation Group 4 () 5
12:10 Creation of groups for Selected Readings 2 and 3
12:30 End

If you don’t have a group, join one of the above by helping edit and comment on it’s google slide set.
We discuss the articles:

TBD
TBD

Each group prepares a set of google slides summarising the chapters assigned to the group. Each summary can have a maximum of 10 slides. Groups self organize their division of work and presentation. 

Schedule (CX denotes chapters in the article, login to see the links to the groups' slides):

09:00 Welcome
09:15 C1 C2 Presentation Group 1 () 5
09:50 C3 C4 C5 Presentation Group 2 () 6
10:30 Break
11:00 C6 C7 Presentation Group 3 () 5
11:35 C8 C9 Presentation Group 4 () 5
12:30 End

If you don’t have a group, join one of the above by helping edit and comment on it’s google slide set.
PD Dr. Sigve Haug

Sigve studied physics in Germany, Spain and Norway. He has been involved in neutrino physics experiments and high energy frontier experiments, often with main focus on the computing challenges related to the large and distributed data from these experiments. Today he is heading the Data Science Lab at the University of Bern.